Current rapid diagnostic for drug resistant TB detect genetic signatures of M. tuberculosis (MTB) and the mutations that cause drug resistance. Gaps in our knowledge of the mutations that lead to resistance to some first and second line drugs limit the ability of these tools to aid clinicians in choosing effective drug regimens early in the course of disease. The goal of this project is to identify novel mutations that can close the existing gap in the sensitivity of molecular diagnosis of MTB drug resistance, to understand the association between individual and combinations of mutation and quantitative drug resistance and to develop and validate a prediction model that will define the optimal set of mutations to be assessed to improve the performance of rapid molecular diagnostics. In previous work, we have identified over 500 MTB strains for which at least one drug resistance phenotype is unexplained by mutations in the known or suspected resistance genes. In Aim 1 of this study, we will more precisely define the resistance phenotype by performing MICs and conduct WGS to identify mutations that may encode resistance. In aim 2, we will expand quantitative resistance phenotyping and perform targeted sequencing of novel drug resistance targets, analyzing these data to determine the impact of individual and interacting mutations on quantitative resistance phenotypes and to develop a model to identify the optimal set of mutations to be included in molecular diagnostics. In Aim 3, we will validate this model and link it to patient treatment outcomes using a new set of strains that we have prospectively collected in the course of an ongoing NIH-funded longitudinal cohort study in Lima, Peru. Data from this study will inform product development of a proposed micro-array based diagnostic and a study that will perform single point mutagenesis of putative drug resistance determinants and subsequently phenotype the resulting mutants.